The single most common question we get is some version of "will this run on my GPU?" The good news for 2026: thanks to GGUF quantization, block-swapping, and distilled models, you can run state-of-the-art image, video, 3D, and voice models on as little as 6GB of VRAM. This guide breaks down what each VRAM tier can realistically do, with the actual requirements for the models we build one-click installers for.
VRAM tiers at a glance
| VRAM | What you can run | Example GPUs |
|---|---|---|
| 4–6 GB | Most image generation (Z-Image, Flux GGUF, SD/Forge), WAN 2.2 video via GGUF, fast 4-step editors, and nearly all local TTS / voice cloning. | RTX 3050/3060, RTX 4050, RTX 2060 |
| 8–10 GB | Smoother video generation (LTX-2 via Wan2GP), higher resolutions, less aggressive quantization, more headroom for batching. | RTX 3070/4060 Ti, RTX 3080 |
| 12–16 GB | Larger video models at higher fidelity (LTX 2.3 22B), 3D asset generation (Trellis 2), full-precision captioning, comfortable LoRA training. | RTX 4070 Ti Super, RTX 4080, RTX 5070 Ti |
| 24 GB+ | The heaviest models with no compromises: Flux 2 Dev, long high-resolution video, big-batch training, multiple models loaded at once. | RTX 3090/4090, RTX 5090 |
Image generation VRAM requirements
Image models are the most accessible category. With GGUF quantization, even 6GB cards run modern models comfortably.
| Model | Min VRAM | Notes |
|---|---|---|
| Z-Image Turbo | 6 GB | ~1 min/image via GGUF; one of the fastest local image models |
| Stable Diffusion Forge UI Neo | 4–6 GB | Optimized Forge build; Sage + Flash Attention 2 + Triton |
| Flux 2 Klein 9B / 4B | 6 GB | FP8 models; ~30s on RTX 4090, runs on 6GB |
| Ernie Image 8B | 6 GB | Baidu's 8B image model, GGUF for low VRAM |
| Flux 2 Dev | 24 GB | Full-quality Flux 2; tuned for RTX 4090 / 24GB |
Video generation VRAM requirements
Video is the most VRAM-hungry category, but GGUF + block-swap have made it surprisingly accessible. WAN 2.2 in particular runs on 6GB.
| Model | Min VRAM | Notes |
|---|---|---|
| WAN 2.2 14B (Text/Image to Video) | 6 GB | GGUF models; the low-VRAM video workhorse |
| WAN 2.2 SVI (Infinite Video) | 6 GB | Expandable 20-second+ generation, low-VRAM ready |
| LTX-2 19B via Wan2GP | 8 GB | Runs LTX-2 19B on modest hardware via Wan2GP |
| LTX 2.3 22B | 16 GB | GGUF models tuned for 16GB cards |
3D asset generation
| Model | Min VRAM | Notes |
|---|---|---|
| Trellis 2 | 16 GB (24 GB rec.) | Microsoft's 3D mesh model; assets in ~2 min on RTX 4090 |
| SPAR3D | ~8–12 GB | Single-image to 3D, fast generation |
Text-to-speech & voice cloning
TTS is the lightest category — many models run on 1–4GB, so almost any modern GPU (and some CPUs) will do.
| Model | Min VRAM | Notes |
|---|---|---|
| Soprano-80M Realtime TTS | ~1 GB | Real-time speech on minimal hardware |
| Chatterbox TTS | ~4–6 GB | Open-source ElevenLabs alternative with voice cloning |
| Qwen3 TTS Voice Clone Studio | ~6 GB | High-quality voice cloning from short clips |
It's not just VRAM — also plan for system RAM and disk
How we hit these low numbers: GGUF, block-swap & distillation
Three techniques make 6GB local AI possible in 2026:
FAQ
Can I run ComfyUI on 6GB VRAM? Yes — most image models and WAN 2.2 video run on 6GB using GGUF models and block-swapping. It's the modern entry point for local AI.
What's the minimum VRAM for WAN 2.2? 6GB, using the GGUF models in our WAN 2.2 ComfyUI and Forge Neo installers.
Do I need 24GB? Only for the heaviest models (Flux 2 Dev, long high-res video) at full quality, or for training large LoRAs. Most tasks are comfortable at 6–16GB.